Sparsity Optimization
Also known as: Pruning
Sparsity optimization, often achieved through pruning, is a technique aimed at reducing the computational cost and memory footprint of a model by identifying and removing less important parameters (weights) or connections.
While pruning is typically applied to models post-training or during fine-tuning for inference efficiency, the concept of sparsity can also be leveraged during distributed training.
For instance, if gradients are sparse (i.e., many gradient values are zero or near-zero), communication-efficient algorithms can be designed to only transmit the non-zero values and their indices, significantly reducing communication volume. Some research also explores methods for inducing sparsity during training itself, leading to models that are inherently sparse.
In distributed settings, especially with large models, sparsity can offer benefits in terms of reduced memory for storing parameters and activations, and potentially faster computation if specialized hardware or software can exploit the sparse structure. However, maintaining and operating on sparse data structures can introduce its own overhead, and the effectiveness of pruning depends heavily on the model architecture and the specific pruning criteria used. The goal is to remove redundant or insignificant parameters without significantly degrading the model's performance.